diff --git a/align_data/embeddings/embedding_utils.py b/align_data/embeddings/embedding_utils.py
index ff38632..54acf26 100644
--- a/align_data/embeddings/embedding_utils.py
+++ b/align_data/embeddings/embedding_utils.py
@@ -1,5 +1,5 @@
 import logging
-from typing import List, Tuple, Dict, Any, Optional
+from typing import List, Tuple, Dict, Any, Optional, Callable
 from functools import wraps
 
 import openai
@@ -93,11 +93,31 @@ def _single_batch_moderation_check(batch: List[str]) -> List[ModerationInfoType]
     return openai.Moderation.create(input=batch)["results"]
 
 
-def moderation_check(texts: List[str], max_texts_num: int = 32) -> List[ModerationInfoType]:
-    """Batch moderation checks on list of texts."""
+def moderation_check(texts: List[str], max_batch_size: int = 4096, tokens_counter: Callable[[str], int] = len) -> List[ModerationInfoType]:
+    """Batch moderation checks on list of texts.
+
+    :param List[str] texts: the texts to be checked
+    :param int max_batch_size: the max size in tokens for a single batch
+    :param Callable[[str], int] tokens_counter: the function used to count tokens
+    """
+    # A very ugly loop that will split the `texts` into smaller batches so that the
+    # total sum of tokens in each batch will not exceed `max_batch_size`
+    parts = []
+    part = []
+    part_count = 0
+    for item in texts:
+        if part_count + tokens_counter(item) > max_batch_size:
+            parts.append(part)
+            part = []
+            part_count = 0
+        part.append(item)
+        part_count += tokens_counter(item)
+    if part:
+        parts.append(part)
+
     return [
         result
-        for batch in (texts[i : i + max_texts_num] for i in range(0, len(texts), max_texts_num))
+        for batch in parts
         for result in _single_batch_moderation_check(batch)
     ]